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J. Risk Financial Manag. 2017, 10(1), 6; doi:10.3390/jrfm10010006

Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model

1
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul (IFRS), Campus Viamão, Av Sen Salgado Filho, 7000, Bairro Sáo Lucas, 94440-000, Viamão, RS, Brazil
2
Universidade Federal do Rio Grande do Sul (UFRGS), Escola de Administração, Rua Washington Luiz, 855, Centro Histórico, 90010-460, Porto Alegre, RS, Brazil
3
Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Pesquisas Hidráulicas (IPH), Av Bento Gonçalves, 9500, Bairro Agronomia, 91501-970, Porto Alegre, RS, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Michael McAleer
Received: 22 August 2016 / Revised: 18 January 2017 / Accepted: 19 January 2017 / Published: 5 February 2017
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Abstract

Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%. View Full-Text
Keywords: modeling financial indicators; NYSE indexes; self organizing maps; multilayer perceptron; back propagation algorithm; software Matlab modeling financial indicators; NYSE indexes; self organizing maps; multilayer perceptron; back propagation algorithm; software Matlab
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Beluco, A.; Bandeira, D.L.; Beluco, A. Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model. J. Risk Financial Manag. 2017, 10, 6.

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